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Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation
Thesis (PhD)--University of Pretoria, 2012.
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University of Pretoria
2013
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| _version_ | 1867613671754039296 |
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| access_status_str | Open Access |
| author2 | Engelbrecht, Andries P. |
| author_browse | Engelbrecht, Andries P. |
| author_facet | Engelbrecht, Andries P. |
| collection | Thesis |
| dc_rights_str_mv | © 2012 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Thesis (PhD)--University of Pretoria, 2012. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/28161 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:39:51.634Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/28161 Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation Engelbrecht, Andries P. mgreeff@gmail.com Helbig, Marde Management of boundary constraint violations Performance measures Guide updates Benchmark functions Dynamic multi-objective optimisation Particle swarm optimization (PSO) Vector evaluated particle swarm optimisation UCTD Thesis (PhD)--University of Pretoria, 2012. Most optimisation problems in everyday life are not static in nature, have multiple objectives and at least two of the objectives are in conflict with one another. However, most research focusses on either static multi-objective optimisation (MOO) or dynamic singleobjective optimisation (DSOO). Furthermore, most research on dynamic multi-objective optimisation (DMOO) focusses on evolutionary algorithms (EAs) and only a few particle swarm optimisation (PSO) algorithms exist. This thesis proposes a multi-swarm PSO algorithm, dynamic Vector Evaluated Particle Swarm Optimisation (DVEPSO), to solve dynamic multi-objective optimisation problems (DMOOPs). In order to determine whether an algorithm solves DMOO efficiently, functions are required that resembles real world DMOOPs, called benchmark functions, as well as functions that quantify the performance of the algorithm, called performance measures. However, one major problem in the field of DMOO is a lack of standard benchmark functions and performance measures. To address this problem, an overview is provided from the current literature and shortcomings of current DMOO benchmark functions and performance measures are discussed. In addition, new DMOOPs are introduced to address the identified shortcomings of current benchmark functions. Guides guide the optimisation process of DVEPSO. Therefore, various guide update approaches are investigated. Furthermore, a sensitivity analysis of DVEPSO is conducted to determine the influence of various parameters on the performance of DVEPSO. The investigated parameters include approaches to manage boundary constraint violations, approaches to share knowledge between the sub-swarms and responses to changes in the environment that are applied to either the particles of the sub-swarms or the non-dominated solutions stored in the archive. From these experiments the best DVEPSO configuration is determined and compared against four state-of-the-art DMOO algorithms. Computer Science unrestricted 2013-09-07T12:59:58Z 2012-09-26 2013-09-07T12:59:58Z 2012-09-06 2012-09-26 2012-09-24 Thesis Helbig, M 2012, Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation, PhD thesis, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/28161 > D12/9/257/ag http://hdl.handle.net/2263/28161 http://upetd.up.ac.za/thesis/available/etd-09242012-211127/ © 2012 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf application/pdf University of Pretoria |
| spellingShingle | Management of boundary constraint violations Performance measures Guide updates Benchmark functions Dynamic multi-objective optimisation Particle swarm optimization (PSO) Vector evaluated particle swarm optimisation UCTD Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation |
| title | Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation |
| title_full | Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation |
| title_fullStr | Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation |
| title_full_unstemmed | Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation |
| title_short | Solving dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation |
| title_sort | solving dynamic multi objective optimisation problems using vector evaluated particle swarm optimisation |
| topic | Management of boundary constraint violations Performance measures Guide updates Benchmark functions Dynamic multi-objective optimisation Particle swarm optimization (PSO) Vector evaluated particle swarm optimisation UCTD |
| url | http://hdl.handle.net/2263/28161 http://upetd.up.ac.za/thesis/available/etd-09242012-211127/ |